System and method for determining sensor margins and/or diagnostic information for a sensor

ABSTRACT

Systems and techniques for determining sensing margins and/or diagnostic information associated with a sensor are presented. A statistics component generates statistical data based on sensor data associated with a sensing device. A margin component generates sensing margins for the sensing device based on the statistical data. An output component generates an indicator for a changing condition associated with the sensing device based on the sensing margins. In an aspect, a diagnostic component generates diagnostic data for the sensing device based on the statistical data.

BACKGROUND

The subject matter disclosed herein relates generally to sensor devices,and, more particularly, to determining sensing margins and/or diagnosticinformation associated with a sensor.

BRIEF DESCRIPTION

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview nor is intended to identify key/critical elements orto delineate the scope of the various aspects described herein. Its solepurpose is to present some concepts in a simplified form as a prelude tothe more detailed description that is presented later.

In one or more embodiments, a system includes a statistics component, amargin component and an output component. The statistics componentgenerates statistical data based on sensor data associated with asensing device. The margin component generates sensing margins for thesensing device based on the statistical data. The output componentgenerates an indicator for a changing condition associated with thesensing device based on the sensing margins.

One or more embodiments also provide a method for generating, by adevice comprising at least one processor, statistical data based onsensor data associated with a sensor of the device, for generating, bythe device, operating margins for the sensor based on the statisticaldata, and for generating, by the device, an indicator for a sensingdecision associated with the sensor based on the operating margins.

Also, according to one or more embodiments, a non-transitorycomputer-readable medium is provided having stored thereon instructionsthat, in response to execution, cause a device comprising a processor toperform operations, the operations comprising generating signaldistribution data for sensor data associated with a sensor device,generating sensing margins for the sensor device based on acharacterization of the signal distribution data, and generating anindicator for a sensing decision associated with the sensor device basedon the sensing margins.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a high-level block diagram of a sensor component, inaccordance with various aspects and implementations described herein.

FIG. 2 illustrates another high-level block diagram of a sensorcomponent, in accordance with various aspects and implementationsdescribed herein.

FIG. 3 illustrates yet another high-level block diagram of a sensorcomponent, in accordance with various aspects and implementationsdescribed herein.

FIG. 4 illustrates yet another high-level block diagram of a sensorcomponent, in accordance with various aspects and implementationsdescribed herein.

FIG. 5 illustrates an exemplary signal level distribution.

FIG. 6 illustrates another exemplary signal level distribution.

FIG. 7 illustrates an exemplary sensor system that includes the sensorcomponent, in accordance with various aspects and implementationsdescribed herein.

FIG. 8 is a flowchart of an example methodology for configuringoperating margins for a sensor.

FIG. 9 is a flowchart of an example methodology for determiningdiagnostic data associated with a sensor.

FIG. 10 is a flowchart of an example methodology for configuringoperating margins and/or determining diagnostic data associated with asensor.

FIG. 11 is a flowchart of another example methodology for configuringoperating margins and/or determining diagnostic data associated with asensor.

FIG. 12 is an example computing environment.

FIG. 13 is an example networking environment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding thereof. It may be evident, however, that the subjectdisclosure can be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate a description thereof.

As used in this application, the terms “component,” “system,”“controller,” “interface” are intended to refer to a computer-relatedentity or an entity related to, or that is part of, an operationalapparatus with one or more specific functionalities, wherein suchentities can be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,a hard disk drive, multiple storage drives (of optical or magneticstorage medium) including affixed (e.g., screwed or bolted) or removableaffixed solid-state storage drives; an object; an executable; a threadof execution; a computer-executable program, and/or a computer. By wayof illustration, both an application running on a server and the servercan be a component. One or more components can reside within a processand/or thread of execution, and a component can be localized on onecomputer and/or distributed between two or more computers. Also,components as described herein can execute from various computerreadable storage media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry which is operated by asoftware or a firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can include a processor therein to executesoftware or firmware that provides at least in part the functionality ofthe electronic components. As further yet another example, interface(s)can include input/output (I/O) components as well as associatedprocessor, application, or Application Programming Interface (API)components. While the foregoing examples are directed to aspects of acomponent, the exemplified aspects or features also apply to a system,interface, controller, and the like.

As used herein, the terms “to infer” and “inference” refer generally tothe process of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

Furthermore, the term “set” as employed herein excludes the empty set;e.g., the set with no elements therein. Thus, a “set” in the subjectdisclosure includes one or more elements or entities. As anillustration, a set of controllers includes one or more controllers; aset of data resources includes one or more data resources; etc.Likewise, the term “group” as utilized herein refers to a collection ofone or more entities; e.g., a group of nodes refers to one or morenodes.

Various aspects or features will be presented in terms of systems thatmay include a number of devices, components, modules, and the like. Itis to be understood and appreciated that the various systems may includeadditional devices, components, modules, etc. and/or may not include allof the devices, components, modules etc. discussed in connection withthe figures. A combination of these approaches also can be used.

Sensor devices are central to the operation of numerous modernautomation systems. These sensor devices can interact with industrialdevices on the plant floor of a plant facility (e.g., an industrialfacility) to control automated processes relating to such objectives asproduct manufacture, product processing, material handling, batchprocessing, supervisory control, and other such applications. Sensordevices need to operate in a robust manner while producing reliablesensing information. Sensing information can include, for example,detection of an object that is indicated by switching a state of logicoutput for a sensor device (e.g., turning ON or OFF a logic output of asensor device). Often times, sensing margins (e.g., operating margins)are established for a sensing device during an installation processassociated with a user application (e.g., an industrial application).Establishment of the sensing margins can improve sensing function of thesensor device (e.g., to minimize false detection of an object, tominimize missed detection of an object, etc.). Sensing margins can bebased on signal level perceived by a sensor relative to a thresholdlevel employed by the sensor to make a sensing decision. In certaininstances, a sensor device can include an indicator related to thesensing margins. However, sensing margins of conventional sensor devicescan be affected by noise. For example, noise (e.g., mechanicalvibration, electromagnetic perturbations, etc.) can cause a sensordevice to false trigger or miss a detection if the sensing margins arenot set high enough. Slow moving noise (e.g., dust in the air, buildupof dirt in front of a sensor lens, buildup of welding slag close to aninductive sensor, etc.) can also decrease accuracy and/or performance ofa sensor device (e.g., sensing margin accuracy can be reduced as aresult of noise, robustness of the sensor device and/or sensing marginscan be reduced as a result of noise, etc.).

To address these and other issues, one or more embodiments of thepresent disclosure generate and/or employ statistical informationassociated with a sensor to determine sensing margins and/or diagnosticinformation for a sensor device. In an aspect, statistical meaninformation, standard deviation information, signal level distributioninformation, noise level information, signal to noise ratio information,noise distribution information and/or other statistical information canbe generated and/or employed to determine sensing margins and/ordiagnostic information associated with a sensor device. In anotheraspect, sensing margins and/or diagnostic information for a sensordevice can be determined based on a characterization of signaldistribution information associated with a sensor. The affect of noiseon sensing margins of the sensor can be minimized by generating and/oremploying the statistical information to determine the sensing margins.Therefore, accuracy of sensing margins and/or diagnostic informationassociated with a sensor device can be improved (e.g., level of noise ata decision point can be reduced, number of false alarms associated witha sensor device can be reduced, number of missed detections associatedwith a sensor device can be reduced, repeatability of sensing decisionscan be improved, sensing condition associated with a sensor device canbe allowed to degrade, etc.). Furthermore, overall robustness,reliability and/or performance of sensing function for a sensor devicecan be improved.

FIG. 1 illustrates an example system 100 for configuring sensing marginsassociated with a sensor. The sensor can be a sensor (e.g., a sensordevice) associated with at least one manufacturing process (e.g., atleast one industrial process). System 100 includes at least a sensorcomponent 102, according to one or more embodiments of this disclosure.Although FIG. 1 depicts certain functional components as residing on thesensor component 102, it is to be appreciated that one or more of thefunctional components illustrated in FIG. 1 may reside on a separatedevice relative to the sensor component 102 in some embodiments. Aspectsof the systems, apparatuses, or processes explained in this disclosurecan constitute machine-executable components embodied within machine(s),e.g., embodied in one or more computer-readable mediums (or media)associated with one or more machines. Such components, when executed byone or more machines, e.g., computer(s), computing device(s), automationdevice(s), virtual machine(s), etc., can cause the machine(s) to performthe operations described. The system 100 can be associated with a sensordevice (e.g., an industrial sensor device, a presence sensor device, ameasurement sensor device, a proximity sensor device, an inductiveproximity sensor device, a capacitive proximity sensor device, anultrasonic proximity sensor device, a photosensor device, aphotoelectric sensor device, another type of sensor device, etc.),application solutions, presence sensing, condition sensing, datasensing, safety components, safety relays, a safety system, anotherindustrial device, another industrial process and/or another industrialsystem.

The sensor component 102 can include a statistics component 104, amargin component 106, an output component 108, one or more processors110, and memory 112. In various embodiments, one or more of thestatistics component 104, the margin component 106, the output component108, the one or more processors 110, and the memory 112 can beelectrically and/or communicatively coupled to one another to performone or more of the functions of the sensor component 102. The one ormore processors 110 can perform one or more of the functions describedherein with reference to the systems and/or methods disclosed. In someembodiments, components 104, 106 and 108 can comprise softwareinstructions stored on memory 112 and executed by processor(s) 110.Memory 112 can be a computer-readable storage medium storingcomputer-executable instructions and/or information for performing thefunctions described herein with reference to the systems and/or methodsdisclosed. The sensor component 102 may also interact with otherhardware and/or software components not depicted in FIG. 1. For example,processor(s) 110 may interact with one or more external user interfacedevices, such as a keyboard, a mouse, a display monitor, a touchscreen,another sensor, a network, a safety device, or other such interfacedevices.

The statistics component 104 can generate statistical data (e.g.,statistical information) based on sensor data associated with a sensingcomponent 114. The sensor data can be measurement data associated withthe sensing component 114. The measurement data can be associated withmeasurement (e.g., amplitude, strength, etc.) of a set of electricaloutputs generated by the sensing component 114 in response to a set ofsignals received by the sensing component 114. The sensing component 114can be a device for detecting and/or signaling a changing conditionassociated with an industrial process (e.g., a manufacturing process).The sensing component 114 can be communicatively coupled to the sensorcomponent 102. Alternatively, the sensor component 102 can include thesensing component 114.

The sensing component 114 can be configured with sensing functionality.The sensing component 114 can detect and/or signal a changing condition(e.g., presence of an object, absence of an object, change in distance,etc.) in response to signal(s) received by the sensing component 114.For example, the sensing component 114 can detect presence of an object,detect absence of an object, detect a change in distance associated withan object, detect a change in size associated with an object, detect achange in color associated with an object, etc. The sensing component114 can therefore be employed to monitor and/or control an industrialprocess (e.g., a manufacturing process). The sensing component 114 canbe a sensor device associated with a receiver, a transmitter (e.g., anemitter), a set of transducers and/or signal conditioning functionality.The sensing component 114 can be associated with a presence sensor, ameasurement sensor, a proximity sensor (e.g., an inductive proximitysensor, a capacitive proximity sensor, an ultrasonic proximity sensor,etc.), a photosensor, a photoelectric sensor, or another type of sensor.In an implementation, the sensing component 114 can detect changes basedon ultrasonic transducer pulses generated and received by the sensingcomponent 114 to facilitate determining a changing condition. In anotherimplementation, the sensing component 114 can detect presence or absenceof light generated by a light source (e.g., a light source associatedwith the sensing component 114) to facilitate determining a changingcondition. In yet another implementation, the sensing component 114 candetect changes in amplitude of oscillation associated with an oscillator(e.g., an oscillator associated with a coil and ferrite core assembly,an oscillator associated with a capacitive probe/plate, etc.) tofacilitate determining a changing condition.

In an aspect, the sensor data can be a distribution of measurement dataassociated with the sensing component 114. For example, multiplemeasurements associated with the sensing component 114 can be storedand/or arranged as a distribution of measurements. Therefore, thestatistics component 104 can generate the statistical data based on adistribution of measurement data associated with the sensing component114. In one example, a signal received by the sensing component 114 canbe associated with particular sensor data. Furthermore, one or moreother signals previously received by the sensing component 114 can beassociated with other sensor data. As such, the statistics component 104can generate a distribution of data (e.g., a distribution of measurementdata) associated with the sensing component 114 based on the particularsensor data associated with the signal received by the sensing component114 and the other sensor data associated with the one or more othersignals previously received by the sensing component 114.

The statistical data generated by the statistics component 104 cancharacterize the sensor data associated with the sensing component 114.For example, the statistical data generated by the statistics component104 can characterize the distribution of the measurement data associatedwith the sensing component 114. In an aspect, the statistical data canbe signal distribution characteristics of the sensor data (e.g., themeasurement data) associated with the sensing component 114. Forexample, the statistical data can include statistical mean data,standard deviation data, variation data, noise level data, signal tonoise ratio data, noise distribution data, statistical median data,statistical mode data and/or other statistical data. The statisticaldata can be, in one example, signal level statistics of the sensor dataassociated with the sensing component 114. For example, the statisticaldata generated by the statistics component 104 can be associated withsignal strength (e.g., amplitude) of the sensor data. Therefore, thestatistics component 104 can generate the statistical data based onamplitude of signal data associated with the sensing component 114. Incertain implementations, the statistics component 104 can generateprobability density information corresponding to probability that asignal received by the sensing component 114 is associated with aparticular amplitude based on the sensor data. For example, thestatistics component 104 can generate a signal level distribution basedon the sensor data. The signal level distribution can be associated withat least a first state and a second state of the sensing component 114(e.g., in certain implementations a signal level distribution can beassociated with more than two states). The first state can be, forexample, a low state associated with a low state threshold and thesecond state can be a high state associated with a high state threshold.The statistics component 104 can generate the signal level distributionbased on a plurality of signals received and/or generated by the sensingcomponent 114.

The margin component 106 can generate and/or modify sensing margins(e.g., operating margins) for the sensing component 114 based on thestatistical data. For example, the margin component 106 can generateand/or modify sensing margins (e.g., operating margins) for the sensingcomponent 114 based on statistical mean data, standard deviation data,variation data, noise level data, signal to noise ratio data, noisedistribution data, statistical median data, statistical mode data and/orother statistical data associated with the sensor data. In certainimplementations, the margin component 106 can generate and/or modifysensing margins (e.g., operating margins) for the sensing component 114based on a characterization of probability density information (e.g., asignal level distribution, signal distribution data, etc.) associatedwith the sensor data. In an aspect, the margin component 106 cangenerate the sensing margins for the sensing component 114 based on acharacterization of probability density information relative topreviously determined sensing margins for the sensing component 114. Themargin component 106 can generate the sensing margins for the sensingcomponent 114, for example, based on a statistical mean data and/orstandard deviation data associated with the probability densityinformation relative to previously determined sensing margins for thesensing component 114. Additionally or alternatively, the margincomponent 106 can generate and/or modify the sensing margins for thesensor based on noise level of the sensor data, a signal to noise ratioof the sensor data and/or noise distribution of the sensor data.However, it is to be appreciated that the margin component 106 cangenerate the sensing margins for the sensing component 114 based onother statistical data associated with the sensor data and/or thesensing component 114.

The output component 108 can generate an indicator for a changingcondition (e.g., a sensing decision) associated with the sensingcomponent 114 based on the sensing margins and/or the statistical data.For example, the output component 108 can generate an indicatorassociated with presence of an object, absence of an object, change indistance associated with an object, change in size associated with anobject, change in color associated with an object, etc. based on thesensing margins determined by the margin component 106. An indicatorgenerated by the output component 108 can be associated with binarylogic (e.g., a good margin state or a poor margin state, etc.). Inanother example, an indicator generated by the output component 108 canbe associated with multiple levels of quantization (e.g., more than twolevels of quantization, etc.). In an implementation, the outputcomponent 108 can transmit a state of an indicator to a controller(e.g., a programmable logic controller, etc.) via a digitalcommunication link (e.g., a serial port of an IO-Link, etc.) In anaspect, the output component 108 can be associated with solid-stateoutput. In another aspect, the output component 108 can be associatedwith analog output. The output component 108 can generate an electricalsignal associated with a changing condition (e.g., a sensing decision)based on the sensing margins determined by the margin component 106.Additionally or alternatively, the output component 108 can modulate oneor more light sources (e.g., one or more light-emitting diodes, etc.)based on the sensing margins determined by the margin component 106. Incertain implementations, the output component 108 can generate a signalassociated with a changing condition (e.g., a sensing decision) that cancontrol one or more sensor outputs based on the statistical data and/orthe sensing margins. This can include, for example, sending a controlsignal to an industrial device or controller to perform a controlaction, initiating a safety action (e.g., removing power from anindustrial device, switching a mode of an industrial device, etc.),sending a feedback message to a display device (e.g., a human-machineinterface, a personal mobile device, etc.), or other such output. In anaspect, the sensor component 102 can be employed during a calibrationmode and/or an installation mode associated with at least onemanufacturing process (e.g., at least one industrial process).

FIG. 2 illustrates an example system 200 for configuring sensing marginsassociated with a sensor. The sensor can be a sensor (e.g., a sensordevice) associated with at least one manufacturing process (e.g., atleast one industrial process). System 200 includes at least the sensorcomponent 102. The sensor component 102 can include the statisticscomponent 104, the margin component 106, the output component 108, theone or more processors 110 and the memory 112. The statistics component104 can include at least a signal distribution component 202. In oneimplementation, the sensing component 114 can be communicatively coupledto the sensor component 102. In another implementation, the sensorcomponent 102 can include the sensing component 114.

The signal distribution component 202 can generate signal distributiondata (e.g., signal distribution information) based on the sensor dataassociated with the sensing component 114. For example, the signaldistribution component 202 can generate signal distribution data (e.g.,signal distribution information) based on measurement data associatedwith the sensing component 114. The measurement data associated with thesensing component 114 can include, for example, amplitude (e.g., signallevel, signal strength, etc.) of signals received and/or generated bythe sensing component 114. However, it is to be appreciated that themeasurement data associated with the sensing component 114 can includeother measurements associated with signals received and/or generated bythe sensing component 114.

In an aspect, the signal distribution data can be associated with asignal level distribution (e.g., a signal amplitude distribution)related to the sensing component 114. The signal level distribution canbe associated with probability density data (e.g., probability densityof a signal associated with the sensing component 114 reaching aparticular amplitude). The signal level distribution can define at leasta first state (e.g., a high state) and a second state (e.g., a lowstate) for the sensing component 114. The first state can be associatedwith a first threshold (e.g., a high state threshold) and the secondstate can be associated with a second threshold (e.g., a low statethreshold). The first state (e.g., the high state) can be defined assignal strength associated with the sensing component 114 being abovethe first threshold (e.g., the high state threshold). The second state(e.g., the low state) can be defined as signal strength associated withthe sensing component 114 being below the second threshold (e.g., thelow state threshold). The state threshold (e.g., the high state) can beassociated with a first pattern of the signal distribution data (e.g., apeak associated with the signal distribution data) and the second state(e.g., the low state) can be associated with a second pattern of thesignal distribution data (e.g., another peak associated with the signaldistribution data). A margin associated with the first state and thesecond state of the signal distribution data can define sensing marginsfor the sensing component 114.

FIG. 3 illustrates an example system 300 for configuring sensing marginsassociated with a sensor. The sensor can be a sensor (e.g., a sensordevice) associated with at least one manufacturing process (e.g., atleast one industrial process). System 300 includes at least the sensorcomponent 102. The sensor component 102 can include the statisticscomponent 104, the margin component 106, the output component 108, theone or more processors 110 and the memory 112. The statistics component104 can include the signal distribution component 202 and an analysiscomponent 302. In one implementation, the sensing component 114 can becommunicatively coupled to the sensor component 102. In anotherimplementation, the sensor component 102 can include the sensingcomponent 114.

The analysis component 302 can analyze the signal distribution datagenerated by the signal distribution component 202. For example, theanalysis component 302 can characterize the signal distribution datagenerated by the signal distribution component 202 (e.g., the analysiscomponent 302 can analyze the signal distribution data to identifyspecific patterns and/or characteristics associated with the signaldistribution data). The analysis component 302 can determine signallevel statistics (e.g., signal strength statistics, signal amplitudestatistics, etc.) associated with the signal distribution data.Statistical data determined by the analysis component 302 can include,for example, statistical mean data, standard deviation data, variationdata, noise level data, signal to noise ratio data, noise distributiondata, statistical median data, statistical mode data and/or otherstatistical data associated with the signal distribution data. In anaspect, the margin component can generate and/or modify sensing marginsfor the sensing component 114 based on the analysis of the signaldistribution data by the analysis component 302. For example, the firstthreshold and/or the second threshold associated with the signaldistribution data can be determined and/or modified based on theanalysis of the signal distribution data by the analysis component 302.

FIG. 4 illustrates an example system 400 for determining diagnostic dataassociated with a sensor. The sensor can be a sensor (e.g., a sensordevice) associated with at least one manufacturing process (e.g., atleast one industrial process). System 400 includes at least the sensorcomponent 102. The sensor component 102 can include the statisticscomponent 104, the margin component 106, the output component 108, theone or more processors 110, the memory 112 and/or a diagnostic component402. The statistics component 104 can include the signal distributioncomponent 202 and/or the analysis component 302. In one implementation,the sensing component 114 can be communicatively coupled to the sensorcomponent 102. In another implementation, the sensor component 102 caninclude the sensing component 114.

The diagnostic component 402 can generate diagnostic data based on thestatistical data and/or the sensor data. The diagnostic data can beassociated with, for example, performance of the sensing component 114.For example, measure of characteristics of signal distribution dataand/or change associated with the signal distribution data can be anindicator of robustness and/or possible degradation associated with thesensing component 114. In another example, a change in the statisticaldata and/or the sensor data can be an indication of a change associatedwith a sensor, an application related to a sensor and/or a physical areaaround a sensor. In yet another example, a change in a shape of adistribution can be an indication of a change associated with a sensor,a change associated with an application related to a sensor and/or aphysical area around a sensor, etc. A change in the statistical data,the sensor data and/or a shape of a distribution can be, for example, anindication that preventive maintenance is required. Therefore, thediagnostic component 402 can generate diagnostic data associated withcharacteristics of signal distribution data and/or change associatedwith the signal distribution data. In an aspect, the diagnosticcomponent 402 can generate a signal associated with diagnostic data.For, the diagnostic component 402 can generate a warning signal inresponse to a particular characterization of the signal distributiondata. Therefore, the diagnostic component 402 can generate a warning ofa possible faulty sensor device based on the statistical data and/or thesensor data. The diagnostic component 402 can also send a signalassociated with diagnostic data to an industrial device or controller toperform a control action, initiate a safety action (e.g., removing powerfrom an industrial device, switching a mode of an industrial device,etc.) based on the diagnostic data, send diagnostic data and/or adiagnostic message to a display device (e.g., a human-machine interface,a user device, a personal mobile device, etc.), be employed forpreventative maintenance, be employed for to monitor health of a sensordevice, etc. In an implementation, the statistics component 104 (e.g.,the signal distribution component 202 and/or the analysis component302), the margin component 106, the output component 108 and/or thediagnostic component 402 can be associated with flash memory.

FIG. 5 represents a signal level distribution 500 associated with asensor (e.g., the sensing component 114). For example, the signal leveldistribution 500 can represent signal amplitude distribution (e.g.,signal strength distribution) in each state (e.g., a low state and ahigh state) of the sensing component 114. The signal level distribution500 can relate to a measurement (or measurements) that corresponds to alevel of a signal. However, it is to be appreciated that the signallevel distribution 500 and/or a ‘signal level distribution’ as disclosedherein can be a different type of distribution. In an aspect, the signallevel distribution 500 can be associated with the signal distributioncomponent 202. The signal level distribution 500 can representprobability density of a signal associated with the sensing component114 reaching a particular amplitude. For example, a low state shown insignal level distribution 500 can correspond to absence of a target(e.g., a reflector associated with a sensor) and a high state shown insignal level distribution 500 can correspond to presence of a target(e.g., a reflector associated with a sensor). Noise for a sensorassociated with signal level distribution 500 can be random with anormal distribution N(μ,σ), where μ is mean and σ is standard deviation,and where a first distribution 502 and a second distribution 504 of thesignal level distribution 500 are associated with the same standarddeviation σ.

When in the high state, signal strength is required to go below arelease point threshold A (e.g., a low threshold A) in order for asensor associated with the signal level distribution 500 (e.g., thesensing component 114) to switch to the low state. When in the lowstate, signal strength is required to go above an on point threshold B(e.g., a high threshold B) in order for a sensor associated with thesignal level distribution 500 (e.g., the sensing component 114) toswitch to the high state. For example, an area to the left of therelease point threshold A is a usable range for a first state (e.g., alow state) of a sensor and an area to the right of the on pointthreshold B is a usable range for a second state (e.g., a high state) ofthe sensor. The wider the distribution and the closer to a threshold thesignal is, the more likely the signal will cross the threshold.Variation of signal strength associated with a sensor (e.g., the sensingcomponent 114) can be induced by displacement of a target creating avalid change of state for the sensor. However, variation of signalstrength associated with a sensor (e.g., the sensing component 114) canalso be induced by unwanted changes (e.g., noise) such as mechanicalvibration, electromagnetic perturbations, temperature variations,internal electrical noise, etc. In certain instances (e.g., if noise istoo large or too powerful), an unwanted change of state for the sensorcan occur.

In a non-limiting example, given an operating frequency (e.g., aswitching frequency) at 1 kHz, a goal can be to have distance betweenmean of distributions (e.g., the first distribution 502 and the seconddistribution 504) and a corresponding thresholds at 7σ. For example, theoperating frequency (e.g., the switching frequency) can indicate howfast a sensor is transitioning from one state of the sensor to anotherstate of the sensor (e.g., how many times per second the sensor isdetecting an object and/or a changing state, etc.). An operation can bedefined as transitioning to a different state and returning to anoriginal state. An ideal scenario can occur when thresholds are centeredon a middle between a low state mean μ_(L) equal to 1 and a high statemean μ_(H) equal to 7. If D is a distance between the first distribution502 and the second distribution 504 (e.g., if distance is a differencebetween the low state mean μ_(L) and the high state mean μ_(H) so thatD=μ_(H)−μ_(R)), then distance D=14σ−H where H corresponds to hysteresis.For example, a hysteresis equal to 15% of a signal level associated witha sensor corresponds to about 1σ. Therefore, a usable signal level forthe high state can correspond to 13σ since distance D=13σ whenhysteresis is equal to 1σ. Hysteresis is a difference between distancewhen a target can be detected (e.g., as the target moves towards asensor) and distance the target is required to move away from a sensorto no longer be detected. Accordingly, to avoid false detection when inthe low state for the non-limiting example, a turn-on point thresholdmust be at a minimum 7σ away from the center of the low statedistribution (e.g., the first distribution 502), where σ is the standarddeviation of the signal amplitude distribution for the low state. Assuch, the signal in low state is unlikely to be large enough to switchthe sensor into high state (e.g., crossing the high state threshold).Similarly, when in high state, a target (e.g., a reflector associatedwith a sensor) must be positioned at a distance relative to the sensorthat generates a large enough signal level in order to avoid a misseddetection. Accordingly, for the non-limiting example, the center of thedistribution of the signal in high state (e.g., the second distribution504) must be at least 7σ away from the low threshold. Distances in thesignal strength domain can define sensing margins for a sensor (e.g.,the sensing component 114).

Positions of a signal level distribution relative to decision thresholdsin each state of a sensor relate directly to robustness and reliabilityof a sensor device. The further away a distribution is from a decisionthreshold (e.g., the greater the distance between a distribution and adecision threshold), the more robust the sensor device is for aparticular application. The distance can be measured relative tostandard deviation of the distribution. For example, a sensor device canbe considered robust when center of the distribution is at least 7σ awayfrom a corresponding decision threshold. When considering twodistributions, distance between a High State distribution and a LowState distribution must be greater than 146 in order for the sensordevice to be considered robust. This minimum separation between twodistributions defines a low margin area M as shown in FIG. 6.

FIG. 6 represents a signal level distribution 600 associated with asensor (e.g., the sensing component 114). In an aspect, the signal leveldistribution 600 can be associated with the signal distributioncomponent 202 and the analysis component 302. Outside of the low marginarea M are a usable range for a first state (e.g., a low state) of asensor and a usable range for a second state (e.g., a high state) of thesensor. Being in the usable range for the first state (e.g., a lowstate) or the second state (e.g., a high state) of the sensor provides ahigh margin for improved robustness associated with a sensor device. Thesignal level distribution 600 includes a first distribution 602associated with the low state and a second distribution 604 associatedwith the high state. The first distribution 602 can correspond the firstdistribution 502 included in the signal level distribution 500. Thesecond distribution 604 can correspond to a modified version (e.g., ashifted version) of the second distribution 504 included in the signallevel distribution 500. The threshold level E and the threshold level Fassociated with the signal level distribution 600 can define sensingmargins for a sensor (e.g., the sensing component 114) associated withthe signal level distribution 600. In the non-limiting example shown inFIG. 6, the threshold level E corresponds to the low threshold Aassociated with the signal level distribution 500, and the thresholdlevel F is different than the high threshold B associated with thesignal level distribution 500.

Changes in distribution characteristics can change the low margin area Mand/or robustness for a sensor (e.g., the sensing component 114). In anaspect, the analysis component 302 can determine a measure of robustnessfor a sensor (e.g., the sensing component 114). The measure ofrobustness can correspond to a Signal Margin to Noise Ratio (SMNR). SMNRis defined as a ratio between distance of a center of a distribution tothreshold over standard deviation of the distribution. For example,SMNR=(μ−Threshold)/σ. Each state of a sensor can comprise a SMNR. Forexample, SMNR for a high state of a sensor can be SMNR_H and SMNR for alow state of a sensor can be SMNR_L, where SMNR_H=(μ−T_(L))/σ andSMNR_L=(μ−T_(H))/σ. In an example where hysteresis is below a certainsize, a single threshold can be employed to determine SMNR for the highstate and the low state of the sensor. For example, a SMNR value for thehigh state and the low state of the sensor can beSMNR_H/L=(μ−T_(H/L))/σ, where a sensor thresholdT_(H/L)=(T_(H)+T_(L))/2. In one example, standard deviation is the samefor the high state and the low state of the sensor, and mean for thehigh state and the low state of the sensor are different. In anotherexample, standard deviation is different for the high state and the lowstate of the sensor, and mean for the high state and the low state ofthe sensor are different. In yet another example, SMNR is the same forthe high state and the low state of the sensor if threshold is in themiddle between the mean of the two distributions (e.g., the firstdistribution 602 associated with the low state and the seconddistribution 604 associated with the high state).

In an aspect, the analysis component 302 can determine a SMNR value(e.g., SMNR_s) based on an operating frequency in standard conditions, anoise distribution in standard conditions and/or factory calibrationsettings. For example, a sensing distance can be defined by setting athreshold according to a desired SMNR value for a low state of a sensor.Furthermore, an object can be placed away from the sensor a greaterdistance than the sensing distance for the high state of the sensor toachieve the same SMNR value as the low state of the sensor. Accordingly,a usable sensing range (e.g., Usable Sensing Distance (USD)) can bedefined. Moreover, the threshold T_(H/L) is therefore implemented in themiddle between a usable sensing distance for the low state and the highstate. In another aspect, the analysis component 302 can determine aSMNR value (e.g., SMNR_a) based on an application associated with thesensor and/or an environment associated with the sensor. Therefore, theanalysis component 302 can determine a Margin to Noise (MN) that isdefined as ratio between actual SMNR in the application and referenceSMNR as determined for standard conditions, where MN=(SMNR_a)/(SMNR_s).Accordingly the low margin area M can decrease probability of a falseoutcome associated with a sensing decision, reduce level of noise at adecision point, reduce number of false alarms associated with a sensor,reduce number of missed detections associated with a sensor, improverepeatability of sensing decisions, allow sensing conditions associatedwith a sensor to degrade, etc.

FIG. 7 illustrates an example sensor system 700 that includes the sensorcomponent 102. The sensor system 700 can include a receiver/emitter 702,an analog front-end 704, a microprocessor unit 706 and/or an IO-Link708. The microprocessor unit 706 can include, for example, an ADC 710, aFourier transform component 712, a signal processing component 714and/or a DAC 716. The signal processing component 714 can include and/orbe associated with the sensor component 102. Additionally, the signalprocessing component 714 can include, for example, a signal detectioncomponent 718. In an implementation, the receiver/emitter 702 caninclude one or more transducers that can be configured as a receiverand/or an emitter. For example, magnitude of energy associated with thereceiver/emitter 702 can be converted into a signal that variesproportionally to variations of the energy. In another implementation,the receiver/emitter 702 can include a capacitive probe and/or plate(e.g., sensor electrodes and/or compensator electrodes) that can beconfigured as a receiver and/or an emitter. In yet anotherimplementation, the receiver/emitter 702 can include a light emittingdiode (LED) light detector (e.g., a photodiode, a phototransistor, etc.)and/or an LED light source that can be configured as a receiver and/oran emitter. In yet another implementation, the receiver/emitter 702 caninclude a coil and/or ferrite core assembly that can be configured as areceiver and/or an emitter. A signal generated based on thereceiver/emitter 702 can be received by the analog front-end 704. Theanalog front-end 704 can include an amplifier, a filter (e.g., alow-pass filter, etc.) and/or another component to condition the signal.In an aspect, the receiver/emitter 702 and/or the analog front-end 704can be implemented as a sensing device (e.g., a sensor). For example,the receiver/emitter 702 and/or the analog front-end 704 can beassociated with the sensing component 114.

The microprocessor unit 706 can receive the signal conditioned by theanalog front-end 704 for further processing. For example, the ADC 710can receive the signal conditioned by the analog front-end 704. The ADC710 can be an analog to digital converter that can convert the signal(e.g., an analog signal) into a digital signal. The digital signalgenerated by the ADC 710 can be further processed by the Fouriertransform component 712. For example, the Fourier transform component712 can process the digital signal based on one or more Fouriertransform algorithms. The signal processing component 714 can beconfigured for signal detection to detect and/or signal a changingcondition (e.g., presence of an object, absence of an object, change indistance, etc.) based on the signal received by the microprocessor unit706 (e.g., the signal generated based on the receiver/emitter 702). Forexample, the signal detection component 718 can be associated withdetection decision logic based on sensing margins, a thresholdcomparator, etc. Therefore, the signal detection component 718 canreceive sensor data via the signal generated based on thereceiver/emitter 702 (e.g., the signal generated based on at least onetransducer coupled to the analog front-end 704 and/or the ADC 710, thesignal generated based on a photodiode, the signal generated based on aphototransistor, the signal generated based on a coil and/or ferritecore assembly, etc.). Additionally, the signal processing component 714can include the sensor component 102 to configure sensing margin, asmore fully disclosed herein. For example, the sensor component 102 caninclude the statistics component 104 (e.g., the signal distributioncomponent 202 and/or the analysis component 302), the margin component106, the output component 108 and/or the diagnostic component 402. In animplementation, the signal processing component 714 can be associatedwith flash memory.

In an aspect, sensing margins (e.g., operating margins) and/or anindicator generated by the sensor component 102 can be received by theIO-Link 708. The IO-Link 708 can be associated with point-to-pointcommunication and/or a connectivity protocol to communicate data toother components associated with the sensor system 700. For example, theIO-Link 708 can be a physical interface. In a non-limiting example, theIO-Link 708 can communicate data to an actuator device. The DAC 716 canbe a digital to analog converter that can convert a signal processed bythe signal processing component 714 (e.g., a digital signal) into ananalog signal. In certain implementations, the microprocessor unit 706can additionally be associated with control and/or parametercalibration, a behavioral state machine, one or more IO drivers, one ormore application programming interfaces, a timer, electrically erasableprogrammable read-only memory (EEPROM), random-access memory, amicrocontroller unit, and/or other sensor functionality.

FIGS. 8-11 illustrate various methodologies in accordance with one ormore embodiments of the subject application. While, for purposes ofsimplicity of explanation, the one or more methodologies shown hereinare shown and described as a series of acts, it is to be understood andappreciated that the subject innovation is not limited by the order ofacts, as some acts may, in accordance therewith, occur in a differentorder and/or concurrently with other acts from that shown and describedherein. For example, those skilled in the art will understand andappreciate that a methodology could alternatively be represented as aseries of interrelated states or events, such as in a state diagram.Moreover, not all illustrated acts may be required to implement amethodology in accordance with the innovation. Furthermore, interactiondiagram(s) may represent methodologies, or methods, in accordance withthe subject disclosure when disparate entities enact disparate portionsof the methodologies. Further yet, two or more of the disclosed examplemethods can be implemented in combination with each other, to accomplishone or more features or advantages described herein.

FIG. 8 illustrates an example methodology 800 for configuring operatingmargins for a sensor. Initially, at 802, statistical data is generatedbased on sensor data associated with a sensor. For example, statisticalmean data, standard deviation data, variation data, noise level data,signal to noise ratio data, noise distribution data, statistical mediandata, statistical mode data and/or other statistical data associatedwith the sensor data can be generated. At 804, operating margins for thesensor are generated based on the statistical data. For example,operating margins for the sensor can be determined and/or modified basedon the statistical data. At 806, an indicator for a sensing decisionassociated with the sensor is generated based on the operating margins.The indicator can be associated with modulation of one or more lightsources (e.g., one or more light-emitting diodes, etc.). Additionally oralternatively, the indicator can be associated with a signal sent to anindustrial device or controller, a message sent to a display device(e.g., a human-machine interface, a user device, a personal mobiledevice, etc.), etc.

FIG. 9 illustrates an example methodology 900 for determining diagnosticdata associated with a sensor. Initially, at 902, sensor data associatedwith a sensor is generated and/or stored. For example, sensor data(e.g., measurement data associated with the sensor) can be generatedand/or stored as signal distribution data. At 904, statistical data isgenerated based on the sensor data. For example, statistical mean data,standard deviation data, variation data, noise level data, signal tonoise ratio data, noise distribution data, statistical median data,statistical mode data and/or other statistical data associated with thesensor data can be generated. At 906, diagnostic data associated withthe sensor is generated based on the statistical data. The diagnosticdata can be associated with, for example, performance of the sensor. Inan aspect, a signal associated with diagnostic data can be generated.For, a warning signal can be generated in response to a particularcharacterization of the signal distribution data. A signal associatedwith diagnostic data can be sent to an industrial device or controllerto perform a control action, can initiate a safety action (e.g.,removing power from an industrial device, switching a mode of anindustrial device, etc.), be associated with a diagnostic message to adisplay device (e.g., a human-machine interface, a user device, apersonal mobile device, etc.), etc.

FIG. 10 illustrates an example methodology 1000 for configuringoperating margins and/or determining diagnostic data associated with asensor. Initially, at 1002, signal distribution data is generated basedon measurement data associated with a sensor. For example, adistribution of measurement data associated with signals received and/orgenerated by the sensor can be determined. At 1004, the signaldistribution data is characterized. For example, statisticalcharacteristics, signal level statistics, and/or patterns associatedwith the signal distribution data can be determined. In an aspect,characterization of the signal distribution data can be associated withstatistical mean data, standard deviation data, variation data, noiselevel data, signal to noise ratio data, noise distribution data,statistical median data, statistical mode data and/or other statisticaldata. At 1006, operating margins for the sensor are generated based onthe characterization of the signal distribution data. At 1008,diagnostic data is generated based on the characterization of the signaldistribution data.

FIG. 11 illustrates an example methodology 1100 for configuringoperating margins and/or determining diagnostic data associated with asensor. Initially, at 1102, signal distribution data is generated basedon measurement data associated with a sensor. At 1104, statistical meandata and/or standard deviation data associated with the signaldistribution data is determined. Additionally, variation data, noiselevel data, signal to noise ratio data, noise distribution data,statistical median data, statistical mode data and/or other statisticaldata associated with the signal distribution data can be determined. At1106, operating margins for the sensor are determined based on thestatistical mean data and/or the standard deviation data associated withthe signal distribution data. At 1108, diagnostic data is generatedbased on the statistical mean data and/or the standard deviation dataassociated with the signal distribution data.

Embodiments, systems, and components described herein, as well asindustrial control systems and industrial automation environments inwhich various aspects set forth in the subject specification can becarried out, can include computer or network components such as servers,clients, programmable logic controllers (PLCs), automation controllers,communications modules, mobile computers, wireless components, controlcomponents and so forth which are capable of interacting across anetwork. Computers and servers include one or more processors—electronicintegrated circuits that perform logic operations employing electricsignals—configured to execute instructions stored in media such asrandom access memory (RAM), read only memory (ROM), a hard drives, aswell as removable memory devices, which can include memory sticks,memory cards, flash drives, external hard drives, and so on.

Similarly, the term controller (e.g., PLC or automation controller) asused herein can include functionality that can be shared across multiplecomponents, systems, and/or networks. As an example, one or morecontrollers (e.g., one or more PLCs or automation controllers) cancommunicate and cooperate with various network devices across a network.This can include substantially any type of control, communicationsmodule, computer, Input/Output (I/O) device, sensor, actuator, and humanmachine interface (HMI) that communicate via the network, which includescontrol, automation, and/or public networks. The controller (e.g., PLCor automation controller) can also communicate to and control variousother devices such as standard or safety-rated I/O modules includinganalog, digital, programmed/intelligent I/O modules, other programmablecontrollers, communications modules, sensors, actuators, output devices,and the like.

The network can include public networks such as the Internet, intranets,and automation networks such as control and information protocol (CIP)networks including DeviceNet, ControlNet, and Ethernet/IP. Othernetworks include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus,Profibus, CAN, wireless networks, serial protocols, and so forth. Inaddition, the network devices can include various possibilities(hardware and/or software components). These include components such asswitches with virtual local area network (VLAN) capability, LANs, WANs,proxies, gateways, routers, firewalls, virtual private network (VPN)devices, servers, clients, computers, configuration tools, monitoringtools, and/or other devices.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 12 and 13 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented.

With reference to FIG. 12, an example environment 1210 for implementingvarious aspects of the aforementioned subject matter includes a computer1212. The computer 1212 includes a processing unit 1214, a system memory1216, and a system bus 1218. The system bus 1218 couples systemcomponents including, but not limited to, the system memory 1216 to theprocessing unit 1214. The processing unit 1214 can be any of variousavailable processors. Multi-core microprocessors and othermultiprocessor architectures also can be employed as the processing unit1214.

The system bus 1218 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, 8-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

The system memory 1216 includes volatile memory 1220 and nonvolatilememory 1222. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1212, such as during start-up, is stored in nonvolatile memory 1222. Byway of illustration, and not limitation, nonvolatile memory 1222 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable PROM (EEPROM), or flashmemory. Volatile memory 1220 includes random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM).

Computer 1212 also includes removable/non-removable,volatile/nonvolatile computer storage media. FIG. 12 illustrates, forexample a disk storage 1224. Disk storage 1224 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-100 drive, flash memory card, or memorystick. In addition, disk storage 1224 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 1224 to the system bus 1218, a removableor non-removable interface is typically used such as interface 1226.

It is to be appreciated that FIG. 12 describes software that acts as anintermediary between users and the basic computer resources described insuitable operating environment 1210. Such software includes an operatingsystem 1228. Operating system 1228, which can be stored on disk storage1224, acts to control and allocate resources of the computer 1212.System applications 1230 take advantage of the management of resourcesby operating system 1228 through program modules 1232 and program data1234 stored either in system memory 1216 or on disk storage 1224. It isto be appreciated that one or more embodiments of the subject disclosurecan be implemented with various operating systems or combinations ofoperating systems.

A user enters commands or information into the computer 1212 throughinput device(s) 1236. Input devices 1236 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1214through the system bus 1218 via interface port(s) 1238. Interfaceport(s) 1238 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1240 usesome of the same type of ports as input device(s) 1236. Thus, forexample, a USB port may be used to provide input to computer 1212, andto output information from computer 1212 to an output device 1240.Output adapters 1242 are provided to illustrate that there are someoutput devices 1240 like monitors, speakers, and printers, among otheroutput devices 1240, which require special adapters. The output adapters1242 include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1240and the system bus 1218. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1244.

Computer 1212 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1244. The remote computer(s) 1244 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1212. For purposes of brevity, only a memory storage device 1246 isillustrated with remote computer(s) 1244. Remote computer(s) 1244 islogically connected to computer 1212 through a network interface 1248and then physically connected via communication connection 1250. Networkinterface 1248 encompasses communication networks such as local-areanetworks (LAN) and wide-area networks (WAN). LAN technologies includeFiber Distributed Data Interface (FDDI), Copper Distributed DataInterface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL).

Communication connection(s) 1250 refers to the hardware/softwareemployed to connect the network interface 1248 to the system bus 1218.While communication connection 1250 is shown for illustrative clarityinside computer 1212, it can also be external to computer 1212. Thehardware/software necessary for connection to the network interface 1248includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 13 is a schematic block diagram of a sample computing environment1300 with which the disclosed subject matter can interact. The samplecomputing environment 1300 includes one or more client(s) 1302. Theclient(s) 1302 can be hardware and/or software (e.g., threads,processes, computing devices). The sample computing environment 1300also includes one or more server(s) 1304. The server(s) 1304 can also behardware and/or software (e.g., threads, processes, computing devices).The servers 1304 can house threads to perform transformations byemploying one or more embodiments as described herein, for example. Onepossible communication between a client 1302 and servers 1304 can be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The sample computing environment 1300 includes acommunication framework 1306 that can be employed to facilitatecommunications between the client(s) 1302 and the server(s) 1304. Theclient(s) 1302 are operably connected to one or more client datastore(s) 1308 that can be employed to store information local to theclient(s) 1302. Similarly, the server(s) 1304 are operably connected toone or more server data store(s) 1310 that can be employed to storeinformation local to the servers 1304.

What has been described above includes examples of the subjectinnovation. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe disclosed subject matter, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the subjectinnovation are possible. Accordingly, the disclosed subject matter isintended to embrace all such alterations, modifications, and variationsthat fall within the spirit and scope of the appended claims.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the disclosed subjectmatter. In this regard, it will also be recognized that the disclosedsubject matter includes a system as well as a computer-readable mediumhaving computer-executable instructions for performing the acts and/orevents of the various methods of the disclosed subject matter.

In addition, while a particular feature of the disclosed subject mattermay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes,” and “including” and variants thereof are used ineither the detailed description or the claims, these terms are intendedto be inclusive in a manner similar to the term “comprising.”

In this application, the word “exemplary” is used to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion.

Various aspects or features described herein may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ],smart cards, and flash memory devices (e.g., card, stick, key drive . .. ).

What is claimed is:
 1. A system, comprising: a memory that storescomputer-executable components; and a processor, operatively coupled tothe memory, that executes the computer-executable components, thecomputer-executable components comprising: a statistics componentconfigured to generate statistical data based on sensor data associatedwith a sensing device; a margin component configured to generate sensingmargins for the sensing device based on the statistical data; and anoutput component configured to generate an indicator for a changingcondition associated with the sensing device based on the sensingmargins.
 2. The system of claim 1, wherein the statistics component isconfigured to generate the statistical data based on a distribution ofmeasurement data associated with the sensing device.
 3. The system ofclaim 1, wherein the statistics component is configured to generate thestatistical data based on amplitude of signal data associated with thesensing device.
 4. The system of claim 1, wherein the statisticscomponent is configured to generate signal distribution data based onthe sensor data.
 5. The system of claim 4, wherein the margin componentis configured to generate the sensing margins for the sensing devicebased on a characterization of the signal distribution data relative topreviously determined sensing margins for the sensing device.
 6. Thesystem of claim 4, wherein the margin component is configured togenerate the sensing margins for the sensing device based on astatistical mean data associated with the signal distribution datarelative to previously determined sensing margins for the sensingdevice.
 7. The system of claim 4, wherein the margin component isconfigured to generate the sensing margins for the sensing device basedon a standard deviation data associated with the signal distributiondata relative to previously determined sensing margins for the sensingdevice.
 8. The system of claim 4, further comprising a diagnosticcomponent configured to generate a warning signal in response to aparticular characterization of the signal distribution data.
 9. Thesystem of claim 1, wherein the margin component is configured togenerate the sensing margins for the sensing device based on noise levelof the sensor data.
 10. The system of claim 1, wherein the margincomponent is configured to generate the sensing margins for the sensingdevice based on a signal to noise ratio of the sensor data.
 11. Thesystem of claim 1, wherein the margin component is configured togenerate the sensing margins for the sensing device based on noisedistribution of the sensor data.
 12. The system of claim 1, furthercomprising a signal detection component that receives the sensor datavia a signal generated based on a transducer coupled to an analog todigital converter.
 13. The system of claim 1, further comprising asignal detection component that receives the sensor data via a signalgenerated based on a photodiode.
 14. A method, comprising: generating,by a device comprising at least one processor, statistical data based onsensor data associated with a sensor of the device; generating, by thedevice, operating margins for the sensor based on the statistical data;and generating, by the device, an indicator for a sensing decisionassociated with the sensor based on the operating margins.
 15. Themethod of claim 14, further comprising: generating, by the device,diagnostic data associated with the sensor based on the statisticaldata.
 16. The method of claim 14, wherein the generating the statisticaldata comprises generating the statistical data based on a distributionof measurement data associated with the sensor.
 17. The method of claim14, wherein the generating the statistical data comprises generating thestatistical data based on amplitude of signal data associated with thesensor.
 18. The method of claim 14, wherein the generating the operatingmargins comprises generating the operating margins for the sensor basedon a characterization of the sensor data.
 19. The method of claim 14,wherein the generating the operating margins comprises generating theoperating margins for the sensor based on statistical mean data andstandard deviation data associated with the sensor data.
 20. Anon-transitory computer-readable medium having stored thereoninstructions that, in response to execution, cause a device comprising aprocessor to perform operations, the operations comprising: generatingsignal distribution data for sensor data associated with a sensordevice; generating sensing margins for the sensor device based on acharacterization of the signal distribution data; and generating anindicator for a sensing decision associated with the sensor device basedon the sensing margins.
 21. The non-transitory computer-readable mediumof claim 20, further comprising: generating diagnostic data associatedwith the sensor device based on the characterization of the signaldistribution data.
 22. The non-transitory computer-readable medium ofclaim 20, wherein the generating the sensing margins comprisesgenerating the sensing margins for the sensor device based onstatistical mean data and standard deviation data associated with thesignal distribution data.